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what the Deep learning and quantum computer
5 cited papers · March 29, 2026 · Powered by Researchly AI
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TL;DR
Deep learning (DL) and quantum computing represent two powerful computational paradigms that researchers are increasingly combining into hybrid frameworks. Deep…
Deep learning (DL) and quantum computing represent two powerful computational paradigms that researchers are increasingly combining into hybrid frameworks. Deep learning allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction, dramatically improving performance across speech recognition, visual object recognition, and object detection.122
Diagram
Current quantum hardware operates in the **Noisy Intermediate-Scale Quantum (NISQ)** era, where devices with 50–100 qubits show promise but are limited by noise and short coherence times.
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Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making.Cai Nan, Zhao Minghui et al.2025Scientific reports
View - Deep Learning — Computational models with multiple processing layers that learn hierarchical data representations, achieving breakthroughs in speech, vision, and other domains.
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Representation learning with parameterised quantum circuits for advancing speech emotion recognition.Rajapakshe Thejan, Rana Rajib et al.2025Scientific reports
View - NISQ Quantum Computing — Quantum devices with 50–100 qubits capable of surpassing some classical tasks, but limited by gate noise, short coherence times, and high error rates.
- Parameterized Quantum Circuits (PQCs) — Variational quantum circuits embedded within classical optimization loops, enabling quantum-enhanced learning while mitigating current hardware constraints.
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Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making.Cai Nan, Zhao Minghui et al.2025Scientific reports
View - Hybrid Quantum–Classical Frameworks — Architectures that integrate quantum circuits with classical neural networks to leverage quantum properties such as superposition and entanglement for enriched feature representations.
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Training deep quantum neural networksKerstin Beer, Dmytro Bondarenko et al.2020Nature Communications
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Diagram
Classical Input Data | v [Classical Preprocessing / CNN Layers] | v [Quantum Encoding Layer] (Superposition + Entanglement) | v [Parameterized Quantum Circuit (PQC)] (Variational / Ansatz) | v [Quantum Measurement / Readout] | v [Classical Optimization Loop] (Gradient updates, error mitigation) | v Output / Decision
Table
| Feature | Classical Deep Learning | Hybrid Quantum–Classical DL |
|---|---|---|
| Core Unit | Artificial neurons / layers | PQCs + classical layers |
| Training | Backpropagation | Variational optimization + classical loops |
| Hardware | GPUs/TPUs | NISQ quantum devices + classical CPUs |
| Key Advantage | Mature, scalable | Potential quantum speedup, richer representations |
| Key Limitation | Data/compute hungry | Noise, limited qubits, coherence issues |
Quantum-enhanced hybrid deep reinforcement learning has demonstrated 95.4% decision accuracy versus 82.1% for classical methods, with a 2.8-fold acceleration in convergence speed.1Hybrid quantum–classical machine learning frameworks that leverage parameterized quantum circuits within classical optimization loops provide a practical pathway toward near-term quantum advantage.2
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Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making.Cai Nan, Zhao Minghui et al.2025Scientific reports
View 2
Representation learning with parameterised quantum circuits for advancing speech emotion recognition.Rajapakshe Thejan, Rana Rajib et al.2025Scientific reports
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Current NISQ devices suffer from limited qubit counts, short coherence times, and high error rates, making fully fault-tolerant quantum algorithms impractical in the near term.1It remains an open question whether variational quantum algorithm (VQA)-based approaches can be competitive with state-of-the-art classical neural networks even on simple benchmark tasks.2
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Noisy intermediate-scale quantum algorithmsKishor Bharti, Alba Cervera-Lierta et al.2022Reviews of Modern Physics
View - Deep learning enables hierarchical representation learning across diverse domains including speech and vision.
- NISQ-era quantum computers show promise but are constrained by noise and limited qubit counts, requiring hybrid approaches.
- PQCs embedded in classical optimization loops form the backbone of hybrid quantum–classical machine learning frameworks.
- Quantum-enhanced reinforcement learning has demonstrated measurable accuracy and convergence improvements over classical baselines.
- Quantum neural networks trained using fidelity as a cost function show efficient training with reduced memory requirements.
1
Representation learning with parameterised quantum circuits for advancing speech emotion recognition.Rajapakshe Thejan, Rana Rajib et al.2025Scientific reports
View 3
Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making.Cai Nan, Zhao Minghui et al.2025Scientific reports
View 4
Training deep quantum neural networksKerstin Beer, Dmytro Bondarenko et al.2020Nature Communications
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- "Variational quantum eigensolver (VQE) for optimization problems in machine learning"
- "Quantum advantage benchmarks for hybrid classical-quantum neural networks"
- "Error mitigation strategies for parameterized quantum circuits in NISQ devices"
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